A founder preparing a tender response for a public sector contract runs the draft through ChatGPT to sense-check the facts. The AI flags nothing. Three weeks later, a procurement officer points out that a market-size figure is four years out of date and the certification claim no longer applies to the current version of the service. The AI did not catch these things because it cannot check what it does not know.
That situation plays out more often than founders expect. It gets worse when the claims concern regulated territory: marketing materials, financial promotions, or any statement carrying an implicit commitment about environmental credentials or pricing comparisons.
The choice you’re facing
When you need to fact-check business claims, no single AI tool handles all of it. The useful question is which type of tool fits the claim and the stakes attached to it. General-purpose language models, specialist claim-detection systems, and structured verification tools each do a different job, and picking the wrong one in a high-stakes context carries real regulatory and commercial risk.
The split is broadly three-way. A general-purpose language model, ChatGPT, Claude, or Gemini, is the lowest-friction starting point but carries the highest risk of confident error on domain-specific or recent facts. Specialist tools like V7 Go are designed to cross-reference claims against your internal data systematically. For regulated territory, including legal, regulatory, or environmental claims, neither category substitutes for human expert judgement.
When a general-purpose AI is the right choice
A general-purpose language model is a reasonable starting point for low-to-medium-stakes content, such as blog posts or internal briefings, when a knowledgeable person will verify every key assertion before publication. The tool helps identify which claims need checking and can suggest candidate sources. Confirming whether those claims are actually true is a separate step, and it belongs with you.
The practical use case is triage. You run a draft through the model and ask it to list every factual assertion in the document. You get a checklist. Then you, or a team member, verifies each item against primary sources: regulator guidance, ONS data, audited accounts, or the relevant supplier contract. The AI shortens the time spent scanning. Replacing the scanning is a different matter.
There are two firm boundaries to hold. First, the AI should not be fed personal data or commercially sensitive client information. The ICO’s guidance on generative AI requires firms to assess cross-border data transfers and conduct a Data Protection Impact Assessment before using these tools on such material. The NCSC similarly advises treating prompts to public AI services as potential data disclosures. Second, anything touching legal, tax, or regulatory interpretation is outside the model’s competence regardless of how confident it sounds. A GPT-4 evaluation study found a non-trivial rate of fabricated facts in complex tasks. That risk is not theoretical.
When a specialist tool is worth the investment
Specialist tools earn their cost when you produce high-volume, high-stakes documents regularly. V7 Go’s AI fact-checking agent extracts claims from documents and cross-references them against an internal knowledge base, citing up to 99% accuracy on structured internal data. That figure needs testing in your own environment before you commit, but the core workflow makes sense for any firm with a standardised document library.
The right conditions for specialist tooling are fairly specific. You need a reasonably consistent internal knowledge base, whether that’s a CRM, a finance system, or a policy library, that the tool can check claims against. You also need a document type that recurs regularly enough to justify integration effort and licence cost. A genuine error problem helps too, such as outdated pricing appearing in tenders, wrong KPI figures in board reports, or inconsistent product specifications across sales materials.
Full Fact, the UK fact-checking charity, is worth understanding in this context. Its automated tools are built for real-time detection of claims in broadcast media and political speech, not for internal business document verification. Products are often marketed with broad “AI fact-checking” language that papers over quite different underlying use cases. Knowing which job a tool was designed for saves a lot of disappointment.
What it costs to get this wrong
The exposure from poorly substantiated claims is concrete in the UK. The ICO can fine firms up to £17.5 million for data misuse in AI systems. The ASA banned HSBC adverts in 2021 for selectively presenting environmental claims, even when individual facts were accurate, because the overall impression was misleading. The CMA investigated ASOS, Boohoo, and George at Asda over sustainability claims that lacked full substantiation. AI-generated copy changes none of this.
The HSBC case is worth sitting with. The bank’s adverts presented accurate individual statements while omitting material that changed their meaning, and the ASA ruled the overall impression was misleading. Whether a claim is misleading depends on what a reasonable person infers from everything surrounding it, not just whether the stated fact is accurate. That contextual analysis is a human judgement. An AI matching strings against a database cannot do it.
Professional indemnity exposure belongs in the same conversation. Misstated claims in tenders or client reports can lead to misrepresentation or breach of contract claims. Hiscox notes that professional indemnity claims frequently arise from incorrect advice or statements that clients relied on to their detriment. AI-assisted drafting that is not properly checked can generate the same exposure.
For UK firms serving EU customers, the EU AI Act adds a further layer. The regulation categorises certain AI systems used in creditworthiness and related decisions as high-risk, with specific documentation and governance requirements covering how facts and evidence are handled.
What to ask before you commit
Before choosing any AI tool for fact-checking, put four questions to the vendor: what the system verifies and how, where your data is processed and stored, whether outputs are designed for meaningful human oversight, and what audit trail is available. If you’re in a regulated sector, add a fifth: whether the FCA or ICO expects you to document the logic behind the system’s outputs.
On the data question, the ICO’s guidance on generative AI is specific: firms must ensure appropriate safeguards for any personal or commercially sensitive data processed by third-party AI systems. Ask any vendor whether it uses documents or prompts to train its models, and whether that can be disabled. The NCSC advises against entering sensitive information into public AI tools unless contractual safeguards are in place.
On the audit trail question, the FCA’s DP5/22 paper on AI makes clear that regulated firms remain responsible for outcomes even when AI tools assist in generating communications or advice. An audit trail showing which sources underpinned each claim is the basis for demonstrating the meaningful human oversight the ICO explicitly requires.
The CMA’s Green Claims Code gives the clearest statement of the evidential standard for environmental and sustainability claims: truthful, accurate, and backed by evidence you can produce and defend. AI tools can help you check whether claims are consistent with evidence you already hold. The evidence itself needs to come from elsewhere, and the person who signs off the final document needs to be confident it is there.
If you want to think through which type of fact-checking approach fits your business, that is worth a direct conversation. Book a conversation and we can work through it.
